jax.linearize

jax.linearize(fun, *primals, has_aux=False)

Produces a linear approximation to fun using jvp() and partial eval.

Parameters:

• fun (Callable) – Function to be differentiated. Its arguments should be arrays, scalars, or standard Python containers of arrays or scalars. It should return an array, scalar, or standard python container of arrays or scalars.

• primals – The primal values at which the Jacobian of fun should be evaluated. Should be a tuple of arrays, scalar, or standard Python container thereof. The length of the tuple is equal to the number of positional parameters of fun.

• has_aux (bool) – Optional, bool. Indicates whether fun returns a pair where the first element is considered the output of the mathematical function to be linearized, and the second is auxiliary data. Default False.

Return type:

tuple[Any, Callable] | tuple[Any, Callable, Any]

Returns:

If has_aux is False, returns a pair where the first element is the value of f(*primals) and the second element is a function that evaluates the (forward-mode) Jacobian-vector product of fun evaluated at primals without re-doing the linearization work. If has_aux is True, returns a (primals_out, lin_fn, aux) tuple where aux is the auxiliary data returned by fun.

In terms of values computed, linearize() behaves much like a curried jvp(), where these two code blocks compute the same values:

y, out_tangent = jax.jvp(f, (x,), (in_tangent,))

y, f_jvp = jax.linearize(f, x)
out_tangent = f_jvp(in_tangent)

However, the difference is that linearize() uses partial evaluation so that the function f is not re-linearized on calls to f_jvp. In general that means the memory usage scales with the size of the computation, much like in reverse-mode. (Indeed, linearize() has a similar signature to vjp()!)

This function is mainly useful if you want to apply f_jvp multiple times, i.e. to evaluate a pushforward for many different input tangent vectors at the same linearization point. Moreover if all the input tangent vectors are known at once, it can be more efficient to vectorize using vmap(), as in:

pushfwd = partial(jvp, f, (x,))
y, out_tangents = vmap(pushfwd, out_axes=(None, 0))((in_tangents,))

By using vmap() and jvp() together like this we avoid the stored-linearization memory cost that scales with the depth of the computation, which is incurred by both linearize() and vjp().

Here’s a more complete example of using linearize():

>>> import jax
>>> import jax.numpy as jnp
>>>
>>> def f(x): return 3. * jnp.sin(x) + jnp.cos(x / 2.)
...
>>> jax.jvp(f, (2.,), (3.,))
(Array(3.26819, dtype=float32, weak_type=True), Array(-5.00753, dtype=float32, weak_type=True))
>>> y, f_jvp = jax.linearize(f, 2.)
>>> print(y)
3.2681944
>>> print(f_jvp(3.))
-5.007528
>>> print(f_jvp(4.))
-6.676704